Charging infrastructure demands of shared-use autonomous electric vehicles in urban areas

Ride-hailing is a clear initial market for autonomous electric vehicles (AEVs) because it features high vehicle utilization levels and strong incentive to cut down labor costs. An extensive and reliable network of recharging infrastructure is the prerequisite to launch a lucrative AEV ride-hailing fleet. Hence, it is necessary to estimate the charging infrastructure demands for an AEV fleet in advance. This study proposes a charging system planning framework for a shared-use AEV fleet providing ride-hailing services in urban area. The authors first adopt an agent-based simulation model, called BEAM, to describe the complex behaviors of both passengers and transportation systems in urban cities. BEAM simulates the driving, parking and charging behaviors of the AEV fleet with range constraints and identifies times and locations of their charging demands. Then, based on BEAM simulation outputs, the authors adopt a hybrid algorithm to site and size charging stations to satisfy the charging demands subject to quality of service requirements. Based on the proposed framework, the authors estimate the charging infrastructure demands and calculate the corresponding economics and carbon emission impacts of electrifying a ride-hailing AEV fleet in the San Francisco Bay Area. The authors also investigate the impacts of various AEV and charging system parameters, e.g., fleet size, vehicle battery capacity and rated power of chargers, on the ride-hailing system’s overall costs.

• Record URL:
  https://doi.org/10.1016/j.trd.2019.102210
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S1361920919303840
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13619209
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhang, Hongcai
  • Sheppard, Colin J R
  • Lipman, Timothy E
  • Zeng, Teng
  • Moura, Scott J
• Publication Date: 2020-1

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Serial:
  • Transportation Research Part D: Transport and Environment
  • Volume: 78
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1361-9209
  • Serial URL: http://www.sciencedirect.com/science/journal/13619209

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Economic impacts; Electric vehicle charging; Electric vehicles; Environmental impacts; Infrastructure; Pollutants; Ridesourcing; Simulation; Urban areas; Vehicle fleets
• Subject Areas: Economics; Energy; Environment; Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01733925
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 20 2020 10:12AM